STAR-RIS-Assisted Full-Space Angle Estimation via Finite Rate of Innovation

TL;DR

The paper addresses full-space DOA estimation in STAR-RIS–assisted ISAC systems, overcoming hemispherical limitations by exploiting STAR-RIS transmission–reflection coupling. It develops two gridless FRI-based recovery schemes: (i) an element-wise uniform ES approach with a rank-one coupling, solved via proximal-gradient denoising and annihilating-filter root finding, and (ii) a nonuniform ES approach using a block-Hankel lifting and paired lifting to handle element-wise amplitude variation with convergence guarantees. A Ziv–Zakai bound is derived to benchmark DOA accuracy, and comprehensive simulations demonstrate accurate, low-overhead angle estimation across RS/TS, outperforming grid-based baselines and illustrating the impact of STAR-RIS aperture and nonuniformity. These results establish a practical, high-resolution framework for full-space sensing and localization in STAR-RIS–enabled ISAC networks, with clear guidance on parameter selection and expected performance.

Abstract

Conventional sensor architectures typically restrict angle estimation to the half-space. By enabling simultaneous transmission and reflection, simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) can support full-space angle detection. This paper develops a fullspace angle estimation framework by leveraging a finite rate of innovation (FRI) model enabled by STAR-RIS. We distinguish two practical STAR-RIS configurations: (i) an element-wise uniform setting, where all metasurface elements share identical energy-splitting (ES) coefficients and phase differences, and (ii) a nonuniform ES setting, where the phase difference is common across elements while the ES coefficients vary element-wise to increase design flexibility. For each regime, we formulate the corresponding FRI-based signal model and derive the Ziv-Zakai bound (ZZB) for angle estimation. To recover the underlying FRI sampling structure, we develop a proximal-gradient algorithm implemented via alternating projections in matrix space and establish its convergence. Exploiting the recovered FRI structure, we construct an annihilating filter whose zeros encode user angles, enabling gridless estimation via polynomial root finding. Numerical results demonstrate that the proposed methods operate reliably across both configuration regimes and achieve improved angle estimation performance with low overhead.

Figures (7)

• Figure 1: Schematic diagram of the STAR-RIS-assisted system.
• Figure 2: Spectrum of the AF equations under different scenarios.
• Figure 3: Root distribution of AF equations (Scenario 2).
• Figure 4: Evaluation of full-space DOA estimation performance.
• Figure 5: Convergence behaviors of the proposed algorithms.